a.k.a. V.J.

The past couple weeks, I’ve been gradually dipping my toe back into baseball, after largely ignoring the sport for the better part of two decades. It’s been fairly easy to pick up where I left off. There have been some rules-changes and I don’t know the majority of this generation of players, but for the most part, it’s been a seamless re-acclimation. The biggest difference I’ve encountered in the MLB of 2023 versus that of 2003 is that a lot of the exotic and advanced statistics that were very much fringe concepts back in the early part of this century are now completely mainstream. Metrics like OPS, WAR, and FIP seem to be as ingrained in everyday analysis of the sport now as batting average, RBIs, and ERA were when I was a kid.

Having spent the past quarter-century working with market-research data and advertising metrics on a daily basis, I am certainly no stranger to analytics. I have also been a casually familiar with advanced baseball statistics going back to the 1980s when I sampled the writings of Bill James, skimmed through Thomas Boswell’s arguments for the Total Average stat; and then years later, read Michael Lewis’ Moneyball before that title had anything to do with Brad Pitt or Jonah Hill.

I mention all this not to claim credentials as some sort of statistician or data scientist. I’m neither of those things. (To be honest, I’m really not even all that good at math.) My point is simply that I’m not a total neophyte when it comes to sports analytics, nor am I stats-averse, or someone who thinks that “too many numbers ruin the game.”

I knew what some of the new metrics were when I started refamiliarizing myself with baseball, but some of them made me feel like I was leafing through class notes from a university course in quantum physics. It quickly became clear that I had some catching up to do.

There are a lot of online resources explaining the stats, including a considerable number of YouTube videos. After some online research, it didn’t take long for me to see how the major metrics were calculated, and to understand the rationales behind them. That said, the sheer volume of new stats still felt like being immersed in so much alphabet soup. It was a good deal of info to process at once and I found myself wishing for a source that could provide a wholistic view of how everything fit together. I wanted a book.

Yesterday, I picked up a copy of A Fan’s Guide to Baseball Analytics by Anthony Castrovince. There were enough positive online reviews to convince me that Castrovince’s book would be a good starting point on the subject. At this writing, I’m about 25% of the way through the book and, so far, I’m finding it worthwhile. It’s accessibly written and treats what could easily be a very dry subject with humor and a nice personal touch.

I’m probably not the target audience for this book. It seems to be written for someone who is coming in a little more skeptical about the value of these new stats, more emotionally attached to the traditional metrics, or just new to baseball statistics in general. Castrovince spends a lot of time pointing out the shortcomings of traditional “back-of-the-baseball-card” stats like Errors, Saves, and pitching Wins. I didn’t need to be convinced that those stats fall short in a lot of ways, but if I had come in skeptical, his arguments probably would have been enough to sway me. I also appreciate that the author points out the limitations of the new metrics, even as he explains how they fill in gaps left by the traditional measurements.

There’s still a lot left in the book for me to digest, but so far it seems to be exactly the one-volume overview I was looking for. I’ve already gained a better understanding of some nuances of the sport and have a new appreciation for some past players. (Who knew that Gene Tenace was an OBP monster?) Analytics are not every baseball fan’s cup of tea, but for anyone who is even remotely curious about them, Castrovince’s book is worth a look.